A Population-Based Analysis of Distant Metastasis in Stage IV Gastric Cancer

Development and validation of an individualized nomogram for predicting distant metastases in gastric cancer using a CT radiomics-clinical model

Purpose

This study aimed to develop and validate a model for accurately assessing the risk of distant metastases in patients with gastric cancer (GC).

Methods

A total of 301 patients (training cohort, n = 210; testing cohort, n = 91) with GC were retrospectively collected. Relevant clinical predictors were determined through the application of univariate and multivariate logistic regression analyses. Then the clinical model was established. Venous phase computed tomography (VPCT) images were utilized to extract radiomic features, and relevant features were selected using univariate analysis, Spearman correlation coefficient, and the least absolute shrinkage and selection operator (Lasso) regression. Subsequently, radiomics scores were calculated based on the selected features. Radiomics models were constructed using five machine learning algorithms according to the screened features. Furthermore, separate joint models incorporating radiomic features and clinically independent predictors were established using traditional logistic regression algorithms and machine learning algorithms, respectively. All models were comprehensively assessed through discrimination, calibration, reclassification, and clinical benefit analysis.

Results

The multivariate logistic regression analysis revealed that age, histological grade, and N stage were independent predictors of distant metastases. The radiomics score was derived from 15 selected features out of a total of 944 radiomic features. The predictive performance of the joint model 1 [AUC (95% CI) 0.880 (0.811-0.949)] constructed using logistic regression is superior to that of the joint model 2 [AUC (95% CI) 0.834 (0.736-0.931)] constructed using SVM algorithm. The joint model 1 [AUC(95% CI) 0.880(0.811-0.949)], demonstrated superior performance compared to the clinical model [AUC(95% CI) 0.781(0.689-0.873)] and radiomics model [AUC(95% CI) 0.740(0.626-0.855), using LR algorithm]. The NRI and IDI values for the joint model 1 and clinical model were 0.115 (95% CI 0.014 -0.216) and 0.132 (95% CI 0.093-0.171), respectively; whereas for the joint model 1 and LR model, they were found to be 0.130 (95% CI 0.018-0.243) and 0.116 (95% CI 0.072-0.160), respectively. Decision curve analysis indicated that the joint model 1 exhibited a higher clinical net benefit than other models.

Conclusions

The nomogram of the joint model, integrating radiomic features and clinically independent predictors, exhibits robust predictive capability for early identification of high-risk patients with a propensity for distant metastases of GC.

Machine learning-based dynamic predictive models for prognosis and treatment decisions in patients with liver metastases from gastric cancer

Gastric cancer with liver metastasis (GCLM) often has a poor prognosis. Therefore, it is crucial to identify risk factors affecting their overall survival (OS) and cancer-specific survival (CSS). This study aimed to construct practical machine learning models to predict survival time and help clinicians choose appropriate treatments. We reviewed the clinical and survival data of GCLM patients from 2010 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) databases and divided the patients into training and testing groups. The risk factors affecting OS and CSS were determined by least absolute shrinkage and selector operator (LASSO), univariate cox regression, best subset regression (BSR) and the stepwise backward regression. Then, five machine learning models, including random survival forest (RSF), Gradient Boosting Machine (GBM), the Cox proportional hazard (CPH), Survival Support Vector Machine (survivalSVM), and eXtreme Gradient Boosting (XGBoost), were built using the identified risk factors. The model with the best predictive ability was determined using concordance index (c-index), area under the curve (AUC), brier score, and decision curve analysis (DCA), and externally verified with data from 233 cases diagnosed with liver metastasis of cancer from The Shijiazhuang People's Hospital, Jinan City People's Hospital, and The Sixth People's Hospital of Huizhou from 2017 to 2018. The study involved a total of 1300 GCLM patients. The prognostic risk factors affecting OS and CSS were the same, including grade, histology, T stage, N stage, surgery, and chemotherapy. The XGBoost model was found to have the best predictive ability for OS, with AUC of 0.891 [95% CI 0.841-0.941], brier score of 0.061 [95% CI 0.046-0.076], and c-index of 0.752 [95% CI 0.742-0.761], as well as for CSS, with AUC of 0.895 [95% CI 0.848-0.942], brier score of 0.064 [95% CI 0.050-0.079], and c-index of 0.746 [95% CI 0.736-0.756]. The AUC score, brier score and c-index all illustrated the accuracy of the model, and the validation using the external datasets further confirmed the reliability of the model. Therefore, the XGBoost model demonstrated significant potential in predicting survival times and selecting appropriate treatment plans.

Survival and Treatment Outcomes in Gastric Cancer Patients with Brain Metastases: A Systematic Review and Meta-Analysis

BACKGROUND

Brain metastases (BM) from gastric cancer (GC) are rare but associated with poor prognosis, significantly impacting patient survival and quality of life. The objective of this systematic review and meta-analysis is to consolidate existing research on BM from GC, evaluate the incidence and clinical outcomes, and explore the effectiveness of treatment options.

METHODS

A systematic search was conducted across the Medline, Web of Science, and Scopus databases, following PRISMA guidelines. Eighteen high-quality studies, as per the Newcastle-Ottawa Quality Assessment Scale, were included, encompassing 70,237 GC patients, of whom 621 developed BM. Data on progression-free survival (PFS), overall survival (OS), neurological symptoms, and HER2 status were analyzed using a random-effects model.

RESULTS

The incidence of BM in GC patients was found to be 2.29% (95% CI: 1.06-3.53%), with the range extending from 0.47% to 7.79% across studies. HER2-positive status was significantly associated with a higher likelihood of developing BM, with an odds ratio of 43.24 (95% CI: 2.05-913.39; p = 0.02), although this finding was based on limited data. The surgical resection of BM was linked to significantly improved survival outcomes, with a mean difference in OS of 12.39 months (95% CI: 2.03-22.75; p = 0.02) compared to non-surgical approaches.

CONCLUSIONS

The surgical resection of brain metastases in GC patients significantly enhances overall survival, while HER2-positive patients may show a higher risk for developing BM. These findings underscore the importance of tailored therapeutic approaches for GC patients with BM.

Blood lipid profiles associated with metastatic sites in advanced gastric cancer

BACKGROUND

This study explored the correlation between peripheral blood lipid levels and clinicopathological parameters in patients with advanced gastric cancer (GC), focusing on changes in lipid levels during disease progression.

METHODS

Pathological features and serum lipid profiles of 179 patients with stage III-IV gastric adenocarcinoma were analyzed. Lipid parameters examined included total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), very low-density lipoprotein cholesterol (VLDL-C), apolipoprotein AI (Apo AI), apolipoprotein B (Apo B), lipoprotein(a) (Lp(a)), among others. The total cholesterol-lymphocyte score (TL score) and BMI were also calculated. The association between lipid parameters and clinicopathological characteristics such as age, gender, family history, and metastasis sites was assessed.

RESULTS

In GC patients, females had higher TG levels than males. Patients with peritoneal metastasis had significantly lower levels of TC, LDL-C, Apo B, and B/A ratio. Those with lung metastasis exhibited higher LDL-C levels and lower levels of VLDL-C. No significant associations were found between lipid levels and metastasis to distant lymph nodes, liver, or bone. Female patients with ovarian metastasis had significantly lower VLDL-C levels. Multivariate analysis revealed low TC as an independent risk factor for peritoneal metastasis, high LDL-C and low VLDL-C levels for lung metastasis, and younger age and low VLDL-C for ovarian metastasis.

CONCLUSION

Specific blood lipid levels are significantly associated with metastatic sites in advanced gastric cancer. Lipid profiles could serve as potential biomarkers for predicting metastatic sites in GC patients.

Downregulation of short-stature homeobox protein 2 suppresses gastric cancer cell growth and stemness in vitro and in vivo via inactivating wnt/β-catenin signaling

Gastric cancer (GC) a prevalent form of cancer globally. Previous research suggests that SHOX2 may have a role in promoting cancer progression. However, the role of SHOX2 in GC is not well understood. Based on data from TCGA_GC data set, SHXO2 levels were examined in normal and GC tissues. Patients in the TCGA_GC cohort were divided into high- and low-SHOX2 level groups for analysis of overall survival (OS), functional enrichment, and immune infiltration. Furthermore, experiments were conducted to investigate the impact of SHOX2 on GC cell function through gain- and loss-of-function experiments. Utilizing data from public databases, SHOX2 mRNA levels were found to be elevated in GC tissues compared to normal control, this finding was confirmed by RT-qPCR, western blot analysis, and immune-histochemical analyses. Elevated SHOX2 levels could serve as an independent indicator of poor prognosis in GC patients. Furthermore, SHOX2 levels had a negative correlation with CD8 T cells and CD4 memory activated T cells, and a positive correlation with of M0 macrophages in GC patients. Functional analyses revealed that SHOX2 deficiency notably suppressed GC cell proliferation, migration, and invasion. Additionally, SHOX2 deficiency was shown to suppress stemness in GC cells in vitro and in vivo via inactivating wnt/β-catenin signaling. Collectively, SHOX2 may serve as a prognostic marker for GC patients, and downregulation of SHOX2 could effectively impede GC cell growth and stemness by inactivating the wnt/β-catenin signaling pathway. These findings underscore the potential of SHOX2 as a promising therapeutic target for GC.

Cellular and molecular mechanisms of gastrointestinal cancer liver metastases and drug resistance

Distant metastases and drug resistance account for poor survival of patients with gastrointestinal (GI) malignancies such as gastric cancer, pancreatic cancer, and colorectal cancer. GI cancers most commonly metastasize to the liver, which provides a unique immunosuppressive tumour microenvironment to support the development of a premetastatic niche for tumor cell colonization and metastatic outgrowth. Metastatic tumors often exhibit greater resistance to drugs than primary tumors, posing extra challenges in treatment. The liver metastases and drug resistance of GI cancers are regulated by complex, intertwined, and tumor-dependent cellular and molecular mechanisms that influence tumor cell behavior (e.g. epithelial-to-mesenchymal transition, or EMT), tumor microenvironment (TME) (e.g. the extracellular matrix, cancer-associated fibroblasts, and tumor-infiltrating immune cells), tumor cell-TME interactions (e.g. through cytokines and exosomes), liver microenvironment (e.g. hepatic stellate cells and macrophages), and the route and mechanism of tumor cell dissemination (e.g. circulating tumor cells). This review provides an overview of recent advances in the research on cellular and molecular mechanisms that regulate liver metastases and drug resistance of GI cancers. We also discuss recent advances in the development of mechanism-based therapy for these GI cancers. Targeting these cellular and molecular mechanisms, either alone or in combination, may potentially provide novel approaches to treat metastatic GI malignancies.

SUV39H1 Regulates Gastric Cancer Progression via the H3K9me3/ALDOB Axis

Gastric cancer (GC) is a malignant tumor with high incidence rate. H3K9me3 is related to transcriptional suppression and modulated by histone methyltransferase suppressor of variegation 3-9 homolog 1 (SUV39H1). SUV39H1 is dysregulated in assorted cancers and exerts the regulatory function. Nevertheless, the specific biofunction of SUV39H1 in GC needs further confirmation. SUV39H1 and H3K9me3 expressions were tested through RT-qPCR and western blot. Colony formation, wound healing, and transwell assays were employed for testing cell behaviors. ChIP assay was utilized for assessing the interaction between H3K9me3 and aldolase B (ALDOB). Xenograft experiment was employed for measuring tumor growth. We found that SUV39H1 and H3K9me3 were overexpressed in GC tissues and cells. SUV39H1 knockdown notably suppressed GC cell proliferative, migratory, and invasive capabilities. The treatment of chaetocin or F5446 (inhibitors of SUV39H1 enzymatic activity) also restrained GC cell behaviors. In addition, we discovered that SUV39H1 could negatively regulate ALDOB expression. SUV39H1 depletion reduced H3K9me3 modification to ALDOB promoter region. In rescue assays, we proved that ALDOB reduction reversed the inhibitory functions of SUV39H1 silencing on GC progression. Furthermore, tumor growth of mice was suppressed by sh-SUV39H1 transfection, chaetocin treatment, or F5446 treatment. In conclusion, SUV39H1 promoted GC progression by modulating the H3K9me3/ALDOB axis.

A Nomogram Based on Clinicopathological Characteristics for Estimating the Risk of Brain Metastasis from Advanced Gastric Cancer: A Multi-Center Retrospective Clinical Study

Purpose

Although brain metastasis (BM) from gastric cancer (GC) is relatively uncommon, its incidence has been increasing owing to advancements in treatment modalities. Unfortunately, patients diagnosed with BM from gastric cancer have poor life expectancy. Our study aims to establish a predictive model for brain metastasis in advanced gastric cancer patients, thus enabling the timely diagnosis of brain metastasis.

Patients and Methods

The clinicopathological features of a cohort which included 40 GC patients with brain metastasis, 32 of whom from the First Affiliated Hospital of Nanchang University, 2 from Gaoxin Branch of the First Affiliated Hospital of Nanchang University, remaining 6 from Anyang District Hospital, and 80 non-metastatic advanced GC patients from the First Affiliated Hospital of Nanchang University between 2018 and 2022. Data were retrospectively analyzed.

Results

Age, tumor size, differentiation, lymph node grade, tumor location, Lauren classification, liver metastasis, carbohydrate antigen 199 (CA199), lactate dehydrogenase (LDH), and human epidermal growth factor receptor 2 (Her-2) were associated with BM. A nomogram integrated with nine risk factors (tumor size, differentiation, lymph node grade, tumor location, Lauren classification, liver metastasis, CA-199, LDH, and Her-2) showed good performance (Area Under Curve 0.95, 95% CI: 0.91-0.98).

Conclusion

We developed and validated a nomogram that achieved individualized prediction of the possibility of BM from GC. This model enables personalized imaging review schedules for timely brain metastasis detection in advanced gastric cancer patients.

Leptomeningeal Carcinomatosis in Early Gastric Cancer: A Case Report and Literature Review

Leptomeningeal carcinomatosis (LC) is a rare site of metastasis in solid tumors, and it is associated with poor prognosis due to disabling symptoms and a scarcity of treatment options. This condition is an uncommon entity in gastric cancer (GC). We present a case of primary LC manifestation in a patient with an incidental diagnosis of localized node-negative GC. We additionally perform a literature review and discuss the diagnostic and therapeutic challenges. In conclusion, LC from GC represents a rare condition with a dramatic prognosis. Its diagnosis might be very challenging. A multidisciplinary approach appears to be the best strategy for the management of LC from GC.

Construction of a nomogram for predicting HNSCC distant metastasis and identification of EIF5A as a hub gene

Patients with distant metastasis of head and neck squamous cell carcinoma (HNSCC) often have a poor prognosis. However, early diagnosis of distant metastasis is challenging in clinical practice, and distant metastasis is often only detected in the late stages of tumor metastasis through imaging techniques. In this study, we utilized data from HNSCC patients collected from the TCGA database. Patients were divided into distant metastasis and nonmetastasis groups based on the tumor-node-metastasis (TNM) stage. We analyzed the differentially expressed genes between the two groups (DM/non-M DEGs) and their associated lncRNAs and generated a predictive model based on 23 lncRNAs that were significantly associated with the occurrence of distant metastasis in HNSCC patients. On this basis, we built a nomogram to predict the distant metastasis of HNSCC patients. Moreover, through WGCNA and Cytoscape software analysis of DM/non-M DEGs, we identified the gene most closely related to HNSCC distant metastasis: EIF5A. Our findings were validated using GEO data; EIF5A expression was significantly increased in the tumor tissues of HNSCC patients with distant metastasis. We then predicted miRNAs that can directly bind to EIF5A via the TargetScan and miRWalk websites, intersected them with differentially expressed miRNAs in the two groups from the TCGA cohort, and identified the only overlapping miRNA, miR-424; we predicted the direct binding site of EIF5A and miR-424 via the miRWalk website. Immunohistochemistry further revealed high expression of EIF5A in the primary tumor tissue of HNSCC patients with distant metastasis. These results provide a new perspective for the early diagnosis of distant metastasis in HNSCC patients and the study of the mechanisms underlying HNSCC distant metastasis.

Cross-talk between gastric cancer and hepatic stellate cells promotes invadopodia formation during liver metastasis

In gastric cancer (GC), the liver is a common organ for distant metastasis, and patients with gastric cancer with liver metastasis (GCLM) generally have poor prognosis. The mechanism of GCLM is unclear. Invadopodia are special membrane protrusions formed by tumor cells that can degrade the basement membrane and ECM. Herein, we investigated the role of invadopodia in GCLM. We found that the levels of invadopodia-associated proteins were significantly higher in liver metastasis than in the primary tumors of patients with GCLM. Furthermore, GC cells could activate hepatic stellate cells (HSCs) within the tumor microenvironment of liver metastases through the secretion of platelet-derived growth factor subunit B (PDGFB). Activated HSCs secreted hepatocyte growth factor (HGF), which activated the MET proto-oncogene, MET receptor of GC cells, thereby promoting invadopodia formation through the PI3K/AKT pathway and subsequently enhancing the invasion and metastasis of GC cells. Therefore, cross-talk between GC cells and HSCs by PDGFB/platelet derived growth factor receptor beta (PDGFRβ) and the HGF/MET axis might represent potential therapeutic targets to treat GCLM.

The polyol pathway and nuclear ketohexokinase A signaling drive hyperglycemia-induced metastasis of gastric cancer

Diabetes might be associated with increased cancer risk, with several studies reporting hyperglycemia as a primary oncogenic stimulant. Since glucose metabolism is linked to numerous metabolic pathways, it is difficult to specify the mechanisms underlying hyperglycemia-induced cancer progression. Here, we focused on the polyol pathway, which is dramatically activated under hyperglycemia and causes diabetic complications. We investigated whether polyol pathway-derived fructose facilitates hyperglycemia-induced gastric cancer metastasis. We performed bioinformatics analysis of gastric cancer datasets and immunohistochemical analyses of gastric cancer specimens, followed by transcriptomic and proteomic analyses to evaluate phenotypic changes in gastric cancer cells. Consequently, we found a clinical association between the polyol pathway and gastric cancer progression. In gastric cancer cell lines, hyperglycemia enhanced cell migration and invasion, cytoskeletal rearrangement, and epithelial-mesenchymal transition (EMT). The hyperglycemia-induced acquisition of metastatic potential was mediated by increased fructose derived from the polyol pathway, which stimulated the nuclear ketohexokinase-A (KHK-A) signaling pathway, thereby inducing EMT by repressing the CDH1 gene. In two different xenograft models of cancer metastasis, gastric cancers overexpressing AKR1B1 were found to be highly metastatic in diabetic mice, but these effects of AKR1B1 were attenuated by KHK-A knockdown. In conclusion, hyperglycemia induces fructose formation through the polyol pathway, which in turn stimulates the KHK-A signaling pathway, driving gastric cancer metastasis by inducing EMT. Thus, the polyol and KHK-A signaling pathways could be potential therapeutic targets to decrease the metastatic risk in gastric cancer patients with diabetes.

Heterogeneous Cellular Response of Primary and Metastatic Human Gastric Adenocarcinoma Cell Lines to Magnoflorine and Its Additive Interaction with Docetaxel

Gastric cancer is the most common cancer and remains the leading cause of cancer death worldwide. In this study, the anticancer action of magnoflorine isolated via counter-current chromatography from the methanolic extract of Berberis vulgaris root against gastric cancer in models of primary ACC-201 and AGS and metastatic MKN-74 and NCI-N87 cell lines was analyzed. Cell viability and proliferation were tested through the use of MTT and BrdU tests, respectively. Cell cycle progression and apoptosis were evaluated using flow cytometry. The interaction of magnoflorine and docetaxel has been examined through isobolographic analysis. Moreover, potential toxicity was verified in zebrafish in an in vivo model. Gastric cancer cell lines revealed different responses to magnoflorine treatment with regard to viability/proliferation, apoptosis induction and cell cycle inhibition without any undesirable changes in the development of larval zebrafish at the tested concentrations. What is more, magnoflorine in combination with docetaxel produced an additive pharmacological interaction in all studied gastric cancer cell lines, which may suggest a complementary mechanism of action of both compounds. Taken together, these findings provide a foundation for the possibility of magnoflorine as a potential therapeutic approach for gastric cancer and merits further investigation, which may pave the way for clinical uses of magnoflorine.

Application of machine learning algorithm in predicting distant metastasis of T1 gastric cancer

Distant metastasis (DM) is relatively uncommon in T1 stage gastric cancer (GC). The aim of this study was to develop and validate a predictive model for DM in stage T1 GC using machine learning (ML) algorithms. Patients with stage T1 GC from 2010 to 2017 were screened from the public Surveillance, Epidemiology and End Results (SEER) database. Meanwhile, we collected patients with stage T1 GC admitted to the Department of Gastrointestinal Surgery of the Second Affiliated Hospital of Nanchang University from 2015 to 2017. We applied seven ML algorithms: logistic regression, random forest (RF), LASSO, support vector machine, k-Nearest Neighbor, Naive Bayesian Model, Artificial Neural Network. Finally, a RF model for DM of T1 GC was developed. The AUC, sensitivity, specificity, F1-score and accuracy were used to evaluate and compare the predictive performance of the RF model with other models. Finally, we performed a prognostic analysis of patients who developed distant metastases. Independent risk factors for prognosis were analysed by univariate and multifactorial regression. K-M curves were used to express differences in survival prognosis for each variable and subvariable. A total of 2698 cases were included in the SEER dataset, 314 with DM, and 107 hospital patients were included, 14 with DM. Age, T-stage, N-stage, tumour size, grade and tumour location were independent risk factors for the development of DM in stage T1 GC. A combined analysis of seven ML algorithms in the training and test sets found that the RF prediction model had the best prediction performance (AUC: 0.941, Accuracy: 0.917, Recall: 0.841, Specificity: 0.927, F1-score: 0.877). The external validation set ROCAUC was 0.750. Meanwhile, survival prognostic analysis showed that surgery (HR = 3.620, 95% CI 2.164-6.065) and adjuvant chemotherapy (HR = 2.637, 95% CI 2.067-3.365) were independent risk factors for survival prognosis in patients with DM from stage T1 GC. Age, T-stage, N-stage, tumour size, grade and tumour location were independent risk factors for the development of DM in stage T1 GC. ML algorithms had shown that RF prediction models had the best predictive efficacy to accurately screen at-risk populations for further clinical screening for metastases. At the same time, aggressive surgery and adjuvant chemotherapy can improve the survival rate of patients with DM.

Trend of gastric cancer incidence and death rate in adolescents and young adults: A retrospective cohort study based on the Surveillance, Epidemiology, and End Results (SEER) database

BACKGROUND AND AIM

Less attentions are paid into the adolescents and young adults (AYAs) diagnosed with gastric cancer (GC). Our study aims to explore incidence rate trend, death rate trend, and prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in AYAs with GC.

METHODS

A retrospective cohort study based on the Surveillance, Epidemiology, and End Results (SEER) database was performed. Joinpoint regression analysis was used to calculate average annual percent change (AAPC) with 95% confidence interval (CI) for incidence and death rate trends. Prognostic factors for OS and CSS were investigated using Cox proportional hazard model and gray test, respectively, with results shown as hazard ratio (HR) with 95% CI.

RESULTS

Although no statistical significance, incidence rate trend showed a tendency to an increase in AYAs (AAPC = 0.2, 95% CI: -0.2-0.6). All-cause death rate trends were declined both in AYAs and middle-aged and elderly people (MAEP), with AAPC value of -0.6 (95% CI: -0.8 to -0.5) and -0.5 (95% CI: -0.6 to -0.4), respectively. AYAs had a lower risk of all-cause death (HR: 0.87, 95% CI: 0.84-0.90) but a higher risk of GC-specific death (HR: 1.11, 95% CI: 1.07-1.15) than MAEP. Chemotherapy (HR: 1.27, 95% CI: 1.16-1.40) and intraoperative radiation (HR: 2.89, 95% CI: 1.13-7.37) were, respectively, identified as risk factors for worse OS and CSS in AYAs.

CONCLUSIONS

AYAs had a higher risk of GC-specific death, indicating more attention should be paid into AYAs with GC.

MAPK4 silencing in gastric cancer drives liver metastasis by positive feedback between cancer cells and macrophages

Liver metastasis is a major cause of death in gastric cancer patients, but the underlying mechanisms are poorly understood. Through a combination of in vivo screening and transcriptome profiling followed by quantitative RT-PCR and tissue array analyses, we found that mitogen-activated protein kinase 4 (MAPK4) downregulation in gastric cancer tissues from patients is significantly associated with liver metastasis and poor prognosis. The knockdown of MAPK4 in gastric cancer cells promotes liver metastasis in orthotopic mouse models. MAPK4 depletion in gastric cancer cells induces the secretion of macrophage migration inhibitory factor (MIF) to polarize tumor-associated macrophages (TAMs) in orthotopic xenograft tumors. Moreover, TAMs activate epithelial-mesenchymal transition of gastric cancer cells to suppress MAPK4 expression, which further increases MIF secretion to polarize TAMs. Taken together, our results suggest a previously undescribed positive feedback loop between cancer cells and macrophages mediated by MAPK4 silencing that facilitates gastric cancer liver metastasis.

HER2-Positive Gastroesophageal Cancers Are Associated with a Higher Risk of Brain Metastasis

Brain metastasis from gastroesophageal adenocarcinomas (GOCs) is a rare but a devastating diagnosis. Human epidermal growth factor receptor 2 (HER2) is a prognostic and predictive biomarker in GOCs. The association of HER2 with GOC brain metastasis is not known. We performed a retrospective analysis of patients with GOCs with known HER2 status between January 2015 and November 2021. HER2 was assessed on either the primary tumour or metastasis by immunohistochemistry or in situ hybridization. The diagnosis of brain metastasis was made on standard imaging techniques in patients with symptoms or signs. HER2 results were available for 201 patients, with 34 patients (16.9%) HER2 positive. A total of 12 patients developed symptomatic brain metastasis from GOCs, of which 7 (58.3%) were HER2 positive. The development of symptomatic brain metastasis was significantly higher in the HER2-positive GOCs (OR8.26, 95%CI 2.09-35.60; p = 0.0009). There was no significant association of HER2 status and overall survival in patients with brain metastasis. Although the rate of brain metastasis remains low in GOCs, the incidence of symptomatic brain metastasis was significantly higher in patients with HER2-positive tumours.

Associations of Education Level With Survival Outcomes and Treatment Receipt in Patients With Gastric Adenocarcinoma

Background

It remains largely unclear how education level, an important socioeconomic factor, affects prognoses for patients with gastric adenocarcinoma (GAC). We aimed to demonstrate the associations between education level and clinical outcomes in patients with GAC.

Methods

We included a total of 30,409 patients diagnosed with GAC from the Surveillance, Epidemiology, and End Results 18 registry database. Education level, household income, unemployment rate, poverty rate, insurance status, and marital status were selected as sociodemographic variables for the comprehensive analysis. Cox and logistic regression models, Kaplan–Meier curves, and subgroup analyses were the primary statistical methods employed.

Results

A low level of education was correlated with less income, higher unemployment rates, and higher poverty rates (all p < 0.001). The multivariate Cox analysis indicated that a high education level was significantly associated with superior overall survival rates and cancer-specific survival rates in patients with GAC (both p < 0.001). We also corroborated favorable survival outcomes by high education level within almost every clinical and demographic subgroup. Furthermore, chemotherapy combined with surgery could markedly prolong the survival for all patients, including patients of stage IV cancer (both p < 0.001). By using multivariable logistic models, patients in counties with high education levels had a higher probability of chemotherapy receipt (p < 0.001). Contrarily, those in the counties with low levels of education were less likely to receive chemotherapy or undergo surgery (p < 0.001).

Conclusions

Education level was identified and confirmed as an independent predictor of treatment and survival for GAC patients. Efforts are needed to provide effective interventions for those whose educational status is adverse.

Patients with brain metastases from gastric cancer

INTRODUCTION

The aim of this study was to retrospectively evaluate the treatment strategies and possible prognostic factors in patients with brain metastases from gastric cancers.

METHODS

We retrospectively reviewed 24 patients with brain metastases from gastric cancer who were treated at our center between January 2014 and March 2020. Clinicopathological characteristics and clinical outcomes were analyzed.

RESULTS

The median follow-up time was six months (range, 1-40). The median survival time after diagnosis of brain metastases was six months (95% confidence interval: 1.33-10.66). The one- and two-year survival rates were 30.7% and 5.1%, respectively. On univariate analyses, absence of extracranial metastases was significantly associated with better overall survival (OS) but not intracranial progression-free survival (iPFS), while Eastern Cooperative Oncology Group performance status (ECOG PS), whole brain radiotherapy (WBRT) and systemic treatments were significantly associated with better iPFS and OS. Multivariate analysis showed that ECOG PS, extracranial disease and systemic treatments were independent prognostic factors, while only systemic treatment influenced the iPFS. Two patients achieved partial response, 12 patients had progressive disease, and 10 patients remained stable. Local tumor control in our series was achieved in 50%. One patient, who received a combination of camrelizumab and apatinib therapy following whole brain radiotherapy, achieved an obvious partial response.

CONCLUSIONS

ECOG PS, extracranial disease and systemic treatments were independent prognostic factors for gastric cancer patients with brain metastases. The present best treatment for this group of patients was individual comprehensive therapy according to the different situation of each patient.

Current treatment strategies for patients with only peritoneal cytology positive stage IV gastric cancer

Gastric cancer (GC) is one of the most common malignancies worldwide and surgery remains the only potentially curative treatment option for it. Although a significant proportion of GC patients are found with distant metastases already at the initial diagnosis. Peritoneal dissemination is the most common site of metastases. Positive peritoneal cytology (Cy1) is associated with poor long-term outcomes; thus, these patients are considered as stage IV even if macroscopic carcinomatosis is absent. Currently, there is no clear evidence for the most optimal treatment for this distinct subpopulation of the stage IV cohort. Available strategies vary from palliative chemotherapy to upfront gastrectomy. This comprehensive review summarized current evidence of different treatment strategies for Cy1 GC including roles of surgery, systemic and intraperitoneal chemotherapy.

Construction, Validation, and Visualization of Two Web-Based Nomograms to Predict Overall and Cancer-Specific Survival in Patients with Gastric Cancer and Lung Metastases

Background

The lung is one of the most common sites of metastasis in gastric cancer. Our study developed two nomograms to achieve individualized prediction of overall survival (OS) and cancer-specific survival (CSS) in patients with gastric cancer and lung metastasis (GCLM) to better guide follow-up and planning of subsequent treatment.

Methods

We reviewed data of patients diagnosed with GCLM in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. The endpoints of the study were the OS and CSS. We used the “caret” package to randomly divide patients into training and validation cohorts in a 7 : 3 ratio. Multivariate Cox regression analysis was performed using univariate Cox regression analysis to confirm the independent prognostic factors. Afterward, we built the OS and CSS nomograms with the “rms” package. Subsequently, we evaluated the two nomograms through calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Finally, two web-based nomograms were built on the basis of effective nomograms.

Results

The OS analysis included 640 patients, and the results of the multivariate Cox regression analysis showed that grade, chemotherapy, and liver metastasis were independent prognostic factors for patients with GCLM. The CSS analysis included 524 patients, and the results of the multivariate Cox regression analysis showed that the independent prognostic factors for patients with GCLM were chemotherapy, liver metastasis, marital status, and tumor site. The ROC curves, calibration curves, and DCA revealed favorable predictive power in the OS and CSS nomograms. We created web-based nomograms for OS (https://zhenghh.shinyapps.io/aclmos/) and CSS (https://zhenghh.shinyapps.io/aslmcss/).

Conclusions

We created two web-based nomograms to predict OS and CSS in patients with GCLM. Both web-based nomograms had satisfactory accuracy and clinical usefulness and may help clinicians make individualized treatment decisions for patients.

The incidence, risk factors and predictive nomograms for early death among patients with stage IV gastric cancer: a population-based study

Background

Although advances in the treatment of stage IV gastric cancer (GC) patients, some patients were observed to die within 3 months of initial diagnosis. The present study aimed to explore the early mortality and risk factors for stage IV GC and further develop nomograms.

Methods

A total of 2,174 eligible stage IV GC patients were selected from the Surveillance, Epidemiology, and End Results database. Logistic regression analyses were used to determine the risk factors and develop the nomograms to predict all-cause early death and cancer-specific early death. The predictive performance of the nomograms was assessed by receiver operating characteristic curves (ROC), calibration plots and decision curve analyses (DCA) in both training and validation cohorts.

Results

Of 2,174 patients enrolled, 708 died within 3 months of initial diagnosis (n=668 for cancer-specific early death). Early mortality remained stable from 2010-2015. Non-Asian or Pacific Islander (API) race, poorer differentiation, middle sites of the stomach, no surgery, no radiotherapy, no chemotherapy, lung metastases and liver metastases were associated with high risk of both all-causes early death and cancer-specific early death. The nomograms constructed based on these factors showed favorable sensitivity, with the area under the ROC range of 0.816-0.847. The calibration curves and DCAs also exhibited adequate fit and ideal net benefit in prediction and clinical application.

Conclusions

Approximately one-third of stage IV GC patients experienced early death. These associated risk factors and predictive nomograms may help clinicians identify the patients at high risk of early death and be the reference for treatment choices.

Primary tumor resection of metastatic gastric cancer in a multimodal era: Two case reports

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Metastatic gastric cancer has a poor prognosis.

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Multimodal era in gastric cancer has changed the outcomes of those patients.

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Good responders of metastatic gastric cancer should be discussed in multidisciplinary.

Introduction

Although no consensus has been reached on the role of surgical treatment for metastatic gastric cancer, some reports suggest promising results on patients with a small disease volume upon presentation. We present two cases of metastatic disease with a favorable outcome following surgical treatment.

Presentation of cases

The first case presented with an adenocarcinoma of the cardia, which was staged as oligometastatic due to a small liver nodule on segment III. Treatment consisted of neoadjuvant chemotherapy followed by laparoscopic esophagectomy and hepatectomy. The patient remains disease-free 62 months after surgery. Unlike the first case, the second case presented with a large number of liver nodules upon diagnosis, ruling out metastasectomy as a possible treatment. The tumor expressed HER2 receptors and responded favorably to chemotherapy plus trastuzumab for 34 months. At this point, disease progression was observed on the primary site, but the hepatic lesions remained stable. The patient underwent gastrectomy, resumed the chemotherapy regimen, and had a favorable outcome, with stability of the liver metastasis and no local recurrence following primary tumor resection.

Discussion

We illustrate through these two cases the effectiveness of a combined approach featuring perioperative chemotherapy and radical surgery for selected cases of oligometastatic gastric cancer, which we hope will spur further research on the topic.

Conclusion

Systemic treatment in metastatic gastric cancer may represent a novel treatment approach that allows surgical resection of the primary tumor in select cases.